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IEEE International Conference on Systems, Man and Cybernetics

作者： Mengjie Liu Chenyang Bu Shengxing Bai Bingbing Dong Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Tables, as an important means of data storage, are widely used in spreadsheets, web tables, and PDFs. By integrating information from table data with knowledge re-trieved from an external knowledge base, and examining the correspondences between cell values in the table and instances in the knowledge base, we can extract knowledge from the table to augment and enrich the knowledge base. To achieve this goal, we first need to classify table cells based on their functions in the layout. Due to the diverse structures arising from the arrangements of rows and columns, as well as the complexity of content resulting from concise data storage, current automation techniques heavily rely on stylistic features of table cells, such as font or color. Moreover, these methods are rarely experimented with or validated on tables without style features. Recent literature indicates that large language models (LLMs) demonstrate an ability to understand the structure and content of tables in tasks such as table judgment reasoning. Even without extensive feature inputs or pre-training, LLMs still show comparable results to machine learning and deep learning in these tasks. Therefore, this paper attempts to apply LLMs to table cell classification without using other stylistic features. We have designed a 4-component prompt paradigm (Classification Definition, Instruction, Table, Com-pletion), representing respectively the classification definition, task instructions, table data, and result output. We conduct experiments on three datasets CIUS, SAUS, and DEEX for table cell classification with one-shot learning. Our experimental results show that with the assistance of LLMs, better results can be achieved without utilizing stylistic features.

关键词： Deep learning Image color analysis Large language models knowledge based systems Layout Memory One shot learning Benchmark testing Probability density function data mining

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An Initial Study of Machine Learning Underspecification Using Feature Attribution Explainable AI Algorithms: A COVID-19 Virus Transmission Case Study 18th

An Initial Study of Machine Learning Underspecification Usin...

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18th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2021

作者： Hinns, James Fan, Xiuyi Liu, Siyuan Raghava Reddy Kovvuri, Veera Yalcin, Mehmet Orcun Roggenbach, Markus Computer Science Department Swansea University Swansea United Kingdom Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands

ISBN: (纸本)9783030891879

From a dataset, one can construct different machine learning (ML) models with different parameters and/or inductive biases. Although these models give similar prediction performances when tested on data that are currently available, they may not generalise equally well on unseen data. The existence of multiple equally performing models exhibits underspecification of the ML pipeline used for producing such models. In this work, we propose identifying underspecification using feature attribution algorithms developed in Explainable AI. Our hypothesis is: by studying the range of explanations produced by ML models, one can identify underspecification. We validate this by computing explanations using the Shapley additive explainer and then measuring statistical correlations between them. We experiment our approach on multiple datasets drawn from the literature, and in a COVID-19 virus transmission case study. © 2021, Springer Nature Switzerland AG.

关键词： COVID-19

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A Character-Word Graph Attention Networks for Chinese Text Classification 12

A Character-Word Graph Attention Networks for Chinese Text C...

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12th IEEE International Conference on Big knowledge, ICBK 2021

作者： Yang, Shigang Liu, Yongguo University of Electronic Science and Technology of China Knowledge and Data Engineering Laboratory of Chinese Medicine School of Information and Software Engineering Chengdu China

ISBN: (纸本)9781665438582

Text classification is an important task in natural language processing. Different from English, Chinese text owns two representations, character-level and word-level. The former has abundant connotations and the latter owns specific meanings. Current researches often simply concatenated two-level features with little processing and failed to explore the affiliation relation-ship between Chinese characters and words. In this paper, we proposed a character-word graph attention network (CW-GAT) to explore the interactive information between characters and words for Chinese text classification. A graph attention network is adopted to capture the context of sentences and the interaction between characters and words. Extensive experiments on six real Chinese text datasets show that the proposed model outperforms the latest baseline methods. © 2021 IEEE.

关键词： Classification (of information)

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A knowledge Graph Completion Model with Path-Specific Learning

A Knowledge Graph Completion Model with Path-Specific Learni...

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Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Xiulin Zheng Zan Zhang Rui Li Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) and also with the School of Computer Science and Information Engineering Hefei University of Technology Hefei Anhui China Inspur Academy of Science and Technology Shandong China

ISBN: (数字)9798350377613

ISBN: (纸本)9798350377620

knowledge graphs (KGs) play an increasingly im-portant role in many knowledge-aware tasks. However, existing KGs are struggle with incompleteness, which motivates knowledge graph completion (KGC), that is, predicting the lost links between entities based on observed triples. Reasoning over relation paths in incomplete KGs is popular. Nonetheless, some significant issues are still remained to be addressed, such as path noise and ambiguity of inferred relation. To address these problems, we propose a novel path augmented _Reasoning model with avoidance of Path noise and Disambiguation of inferred relation in this paper, referred to as RPD. In this model, we calculate the sum of resource allocation for each relation path to measure its reliability to avoid the inference of path noise. To address the ambiguity of an inferred relation, we introduce position embedding to denote the relation position along the path when learning path representation. Extensive experiments conducted on benchmark datasets demonstrate the effectiveness of our proposal RPD model in the handling of KGC tasks compared to SOTAs.

关键词： Heavily-tailed distribution Noise knowledge graphs Benchmark testing Transformers Cognition Reliability Resource management Proposals Noise measurement

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Autism detection due to environmental pollutants using supervised learning algorithms

Autism detection due to environmental pollutants using super...

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7th IET Smart Cities Symposium (SCS 2023)

作者： R. Surendran M. S. Seena Aravind Swaminathan Joel Benjamin Julius Babu Visal Vinod Department of computer science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Science Chennai India Data Science Department of Computer Science Middlesex University Knowledge Park Dubai UAE

Air pollution, a major global concern resulting in numerous annual fatalities, has been associated with various health disorders. This study focuses on understanding the impact of residing in heavily polluted cities on mental and behavioral patterns, specifically exploring the potential link between air pollution and autism. Supervised classification algorithms, including logistic regression, random forest, decision tree, and AdaBoost, were employed to predict the risk of autism in individuals residing in severely polluted countries by 2030. The research aims to identify the relationship between genetic variations in adults with Autism Spectrum Disorder (ASD) and harmful air pollutants, investigate gender differences in autism frequency, and determine the neurotoxicant posing the greatest danger for neurodegeneration and its impact on autistic individuals. This study successfully employed supervised classification models to uncover hidden patterns between air pollution and autism risk. The binary prediction model achieved an average accuracy of 70%, with the AdaBoost algorithm demonstrating the highest accuracy at 73% in predicting autism prevalence. Increased PM2.5 concentrations correlated with higher autism risk compared to other neurotoxicants. These findings underscore the importance of sustainable practices and pollution reduction to safeguard human health.

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Semantic-Reconstructed Graph Transformer Network for Event Detection

Semantic-Reconstructed Graph Transformer Network for Event D...

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International Joint Conference on Neural Networks (IJCNN)

作者： Zhuochun Miao Xingrui Zhuo Gongqing Wu Chenyang Bu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) School of Computer Science and Information Engineering Hefei University of Technology Anhui China

Event detection (ED) is a key subtask of information extraction to extract key events, such as stock rise and fall and social public opinion, from news or social media. Although current GCN-based event detection methods achieve remarkable success via building graphs with dependency trees, they typically suffer from two challenges: 1) They use sequence models to learn contextual information of sentences, ignoring the longterm dependencies problem of sequence models might learn ineffective information and make it propagate in GCN layers. 2) Most methods do not exploit global dependency label information and grammatical structure information that convey rich linguistic knowledge directly, and only consider local dependency label information. To cope with these challenges, we propose a novel event detection model via semantic-reconstructed graph transformer networks (SRGTNED), which incorporates semantic reconstruction and path information collection methods. Using the semantic reconstruction method, we assign a pruned sequence to each word based on the path information to capture contextual information consistent with sentence semantics. Moreover, to better utilize global dependency label information and grammatical structure information, a Graph Transformer Network (GTN)-based heterogeneous graph embedding framework is introduced to automatically learn path information between important words by converting sentences as heterogeneous graphs. We conduct experiments on the ACE2005 dataset and the Commodity News dataset, and the experimental results demonstrate that our method significantly outperforms 11 state-of-the-art baselines in terms of the F1-score.

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MS²-GNN: Exploring GNN-Based Multimodal Fusion Network for Depression Detection

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IEEE transactions on cybernetics 2023年第12期53卷 7749-7759页

作者： Tao Chen Richang Hong Yanrong Guo Shijie Hao Bin Hu Key Laboratory of Knowledge Engineering with Big Data Ministry of Education and the School of Computer Science and Information Engineering Hefei University of Technology Hefei China Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou China

Major depressive disorder (MDD) is one of the most common and severe mental illnesses, posing a huge burden on society and families. Recently, some multimodal methods have been proposed to learn a multimodal embedding for MDD detection and achieved promising performance. However, these methods ignore the heterogeneity/homogeneity among various modalities. Besides, earlier attempts ignore interclass separability and intraclass compactness. Inspired by the above observations, we propose a graph neural network (GNN)-based multimodal fusion strategy named modal-shared modal-specific GNN, which investigates the heterogeneity/homogeneity among various psychophysiological modalities as well as explores the potential relationship between subjects. Specifically, we develop a modal-shared and modal-specific GNN architecture to extract the inter/intramodal characteristics. Furthermore, a reconstruction network is employed to ensure fidelity within the individual modality. Moreover, we impose an attention mechanism on various embeddings to obtain a multimodal compact representation for the subsequent MDD detection task. We conduct extensive experiments on two public depression datasets and the favorable results demonstrate the effectiveness of the proposed algorithm.

关键词： Task analysis Feature extraction Semantics Depression Graph neural networks Electroencephalography data mining Mental disorders Multisensory integration

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Link Prediction for Opportunistic Networks Based on Hybrid Similarity Metrics and E-LSTM-D Models 24th

Link Prediction for Opportunistic Networks Based on Hybrid...

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24th International Conference on Web Information Systems engineering, WISE 2023

作者： Yang, Xiaoying Li, Lijie Xu, Gang Song, Ming Zhang, Feng College of Computer Science Inner Mongolia University Hohhot010021 China Inner Mongolia A.R. Key Laboratory of Data Mining and Knowledge Engineering Inner Mongolia University Hohhot010021 China School of Computer and Information Technology Shanxi University Taiyuan030000 China

ISBN: (纸本)9789819972531

Link prediction is a crucial issue in opportunistic networks routing research. Static link prediction methods ignore the historical information of network evolution, which affects the prediction accuracy. In this paper, we propose a link prediction method (HS-LSTM) that combines hybrid similarity metrics and the E-LSTM-D model. The approach extracts structural features using hybrid similarity metrics, constructs a structural feature matrix, and applies the E-LSTM-D model to extract temporal features and predict the links that have not yet appeared in the network. Our experiments on three real datasets of opportunistic networks (Infocom5, MIT, and Haggle) shows that our proposed method outperforms the baseline method in terms of prediction accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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Deep Graph Convolutional Networks for Wind Speed Prediction 29

Deep Graph Convolutional Networks for Wind Speed Prediction

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29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2021

作者： Stańczyk, Tomasz Mehrkanoon, Siamak Maastricht University Department of Data Science and Knowledge Engineering Paul-Henri Spaaklaan 1 Maastricht6229 EN Netherlands

ISBN: (纸本)9782875870827

In this paper, we introduce a new model for wind speed prediction based on spatio-temporal graph convolutional networks. Here, weather stations are treated as nodes of a graph with a learnable adjacency matrix, which determines the strength of relations between the stations based on the historical weather data. The self-loop connection is added to the learnt adjacency matrix and its strength is controlled by additional learnable parameter. Experiments performed on real datasets collected from weather stations located in Denmark and the Netherlands show that our proposed model outperforms previously developed baseline models on the referenced datasets. © 2021 ESANN Intelligence and Machine Learning. All rights reserved.

关键词： Wind

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Multivariate Time Series Classification via Hierarchical Graph Embedding

Multivariate Time Series Classification via Hierarchical Gra...

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International Joint Conference on Neural Networks (IJCNN)

作者： Wenhao Niu Xingrui Zhuo Gongqing Wu JunWei Lv Zan Zhang Chenyang Bu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) School of Computer Science and Information Engineering Hefei University of Technology Anhui China

Multivariate time series classification aims to determine the labels for multivariate time series samples. Although variable interaction relationships and sample similarity relationships exist in multivariate time series, the available related methods usually ignore the rich relationships and are ineffective in exploiting these. To solve this problem, we propose a Hierarchical Graph Embedding for Multivariate Time Series Classification (MTSC-HGE), which consists of a variable-wise attentive graph pooling module and a sample-wise graph convolutional module to obtain the relationships of variables and samples. Specifically, we design an attentive graph pooling module based on self-attention, which can obtain sample features fusing temporal patterns and variable interaction relationships in samples. Furthermore, we propose a graph mapping criterion that converts the MTS dataset into a graph based on dynamic time warping to explicitly reflect the similarity relationships between samples. To capture latent sample relationships, a GCN module is utilized on the sample graph to integrate sample features obtained from the attentive graph pool module. In addition, a classifier takes the rich representation output by the model to get the final predicted class. Extensive experiments on 14 public datasets show that MTSC-HGE significantly outperforms state-of-the-art baselines.

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